1. Field of Invention
This invention relates to systems and methods for processing automatically generated recommendations.
2. Description of Related Art
Computers are increasingly being used as tools for information access and communication. We routinely search the World Wide Web for information, locate corporate documents on local Intranets, use our computers to read daily news, send and receive email or shop for products. While the advent of such widespread connectivity has undoubtedly led to a significant productivity increase, it has also introduced new challenges. For example, “information overload” is no longer just a trendy buzzword, but a daily reality for most of us. As a result, approaches that address information overload continue to receive attention in the form of ongoing academic research and novel products.
Within this context, automatically generated recommendations play an important role. We continuously receive these recommendations while we are interacting with information services. For example, we receive product recommendations while we shop: Amazon.com and many other online retailers recommend products similar to the ones just bought or accessed. We receive contextual ads based on our search terms when we search the World Wide Web: displaying ads on search result pages is now a common practice followed by Google and virtually all other search engine providers. Displaying these ads or product suggestions can be interpreted as a “proactive contextual recommendation”—proactive, because the user does not have to explicitly request a recommendation, and contextual because the recommendation is related to the user's current context, e.g. a search on topic X, or the recent acquisition of product Y.
Clearly, proactive contextual recommendations are not limited to product recommendations or advertisements. Information overload and information discovery are issues that extend into the corporate workplace, and as a result, several commercial products as well as research projects have recently focused on proactive information access for corporate knowledge management. For example, products made by knowledge and content management infrastructure vendors such as Autonomy and Verity can proactively recommend documents, often based on implicit techniques designed to anticipate users' information needs. Research prototypes such as Watson, Remembrance Agent, and FAPAL Bar by Fuji Xerox Palo Alto Laboratory (FXPAL) are further examples of systems that provide proactive contextual access to corporate resources.
While the utility of proactive contextual recommendations has been demonstrated within various application scenarios, the approach suffers from two significant problems. First, proactive recommendations are typically communicated via subtle interfaces—the user is supposed to see them, but if they become too obtrusive or distracting, the overall user experience may suffer. Since proactive recommendation interfaces tend to be subtle (e.g. a small ad at the side of the screen, or an icon indicating the availability of related information), users frequently miss potentially useful information. Similarly, users may not wish to interrupt their current task, even if they notice that they just received a recommendation.
Second, contextual recommendations are frequently irrelevant or inaccurate. Since these recommendations are commonly based on limited information, e.g. the user's last search or purchase, and do not require users to explicitly express their information needs, there is a high chance that the recommendation may not be relevant to the user's needs.
Another drawback associated with the current approaches is the lack of persistence. There is typically no obvious way to return to previously recommended items. Users may remember that they previously saw a recommendation that looked promising, but unless they know exactly how recommendations were triggered, they cannot easily return to them.